This class represents a layer of perceptrons. More...

#include <perceptron_layer.h>

Inheritance diagram for PerceptronLayer:

Public Types
enum class	ActivationFunction { Threshold , SymmetricThreshold , Logistic , HyperbolicTangent , Linear , RectifiedLinear , ExponentialLinear , ScaledExponentialLinear , SoftPlus , SoftSign , HardSigmoid }
	Enumeration of available activation functions for the perceptron neuron model. More...

Public Types inherited from Layer
enum class	Type { Scaling , Convolutional , Perceptron , Pooling , Probabilistic , LongShortTermMemory , Recurrent , Unscaling , Bounding }
	This enumeration represents the possible types of layers. More...

Public Member Functions
	PerceptronLayer ()

	PerceptronLayer (const Index &, const Index &, const ActivationFunction &=PerceptronLayer::ActivationFunction::HyperbolicTangent)

virtual	~PerceptronLayer ()

bool	is_empty () const

Index	get_inputs_number () const
	Returns the number of inputs to the layer. More...

Index	get_neurons_number () const
	Returns the number of neurons in the layer. More...

const Tensor< type, 2 > &	get_biases () const

const Tensor< type, 2 > &	get_synaptic_weights () const

Tensor< type, 2 >	get_biases (const Tensor< type, 1 > &) const

Tensor< type, 2 >	get_synaptic_weights (const Tensor< type, 1 > &) const

Index	get_biases_number () const

Index	get_synaptic_weights_number () const
	Returns the number of layer's synaptic weights. More...

Index	get_parameters_number () const
	Returns the number of parameters(biases and synaptic weights) of the layer. More...

Tensor< type, 1 >	get_parameters () const

const PerceptronLayer::ActivationFunction &	get_activation_function () const

string	write_activation_function () const

const bool &	get_display () const

void	set ()

void	set (const Index &, const Index &, const PerceptronLayer::ActivationFunction &=PerceptronLayer::ActivationFunction::HyperbolicTangent)

void	set_default ()

void	set_name (const string &)

void	set_inputs_number (const Index &)

void	set_neurons_number (const Index &)

void	set_biases (const Tensor< type, 2 > &)

void	set_synaptic_weights (const Tensor< type, 2 > &)

void	set_parameters (const Tensor< type, 1 > &, const Index &index=0)
	Sets the parameters of this layer. More...

void	set_activation_function (const ActivationFunction &)

void	set_activation_function (const string &)

void	set_display (const bool &)

void	set_biases_constant (const type &)

void	set_synaptic_weights_constant (const type &)

void	set_parameters_constant (const type &)

void	set_parameters_random ()

void	calculate_combinations (const Tensor< type, 2 > &, const Tensor< type, 2 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_activations (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_activations_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

Tensor< type, 2 >	calculate_outputs (const Tensor< type, 2 > &)

void	forward_propagate (const Tensor< type, 2 > &, LayerForwardPropagation *)

void	forward_propagate (const Tensor< type, 2 > &, Tensor< type, 1 >, LayerForwardPropagation *)

void	calculate_hidden_delta (LayerForwardPropagation , LayerBackPropagation , LayerBackPropagation *) const

void	calculate_hidden_delta_perceptron (PerceptronLayerForwardPropagation , PerceptronLayerBackPropagation , PerceptronLayerBackPropagation *) const

void	calculate_hidden_delta_probabilistic (ProbabilisticLayerForwardPropagation , ProbabilisticLayerBackPropagation , PerceptronLayerBackPropagation *) const

void	calculate_hidden_delta_lm (LayerForwardPropagation , LayerBackPropagationLM , LayerBackPropagationLM *) const

void	calculate_hidden_delta_perceptron_lm (PerceptronLayerForwardPropagation , PerceptronLayerBackPropagationLM , PerceptronLayerBackPropagationLM *) const

void	calculate_hidden_delta_probabilistic_lm (ProbabilisticLayerForwardPropagation , ProbabilisticLayerBackPropagationLM , PerceptronLayerBackPropagationLM *) const

void	calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagationLM )

void	insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const

void	calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagation ) const

void	insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const

string	write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const

string	write_activation_function_expression () const

string	write_expression_c () const

string	write_combinations_c () const

string	write_activations_c () const

string	write_combinations_python () const

string	write_activations_python () const

string	write_expression_python () const

void	from_XML (const tinyxml2::XMLDocument &)

void	write_XML (tinyxml2::XMLPrinter &) const

Public Member Functions inherited from Layer
string	get_name () const

void	set_threads_number (const int &)

virtual Tensor< type, 2 >	calculate_outputs_from4D (const Tensor< type, 4 > &)

virtual Tensor< type, 4 >	calculate_outputs_4D (const Tensor< type, 4 > &)

virtual void	forward_propagate (const Tensor< type, 4 > &, LayerForwardPropagation *)

virtual void	forward_propagate (const Tensor< type, 4 > &, Tensor< type, 1 >, LayerForwardPropagation *)

virtual void	calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation , LayerBackPropagation ) const

Type	get_type () const

string	get_type_string () const
	Takes the type of layer used by the model. More...

Protected Attributes
Tensor< type, 2 >	biases

Tensor< type, 2 >	synaptic_weights
	This matrix containing conection strengths from a layer's inputs to its neurons. More...

ActivationFunction	activation_function
	Activation function variable. More...

bool	display = true
	Display messages to screen. More...

Protected Attributes inherited from Layer
NonBlockingThreadPool *	non_blocking_thread_pool = nullptr

ThreadPoolDevice *	thread_pool_device = nullptr

string	layer_name = "layer"
	Layer name. More...

Type	layer_type = Type::Perceptron
	Layer type. More...

const Eigen::array< IndexPair< Index >, 1 >	A_BT = {IndexPair<Index>(1, 1)}

const Eigen::array< IndexPair< Index >, 1 >	AT_B = {IndexPair<Index>(0, 0)}

const Eigen::array< IndexPair< Index >, 1 >	A_B = {IndexPair<Index>(1, 0)}

Additional Inherited Members
Protected Member Functions inherited from Layer
void	hard_sigmoid (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hyperbolic_tangent (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	logistic (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	symmetric_threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	rectified_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	scaled_exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_plus (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_sign (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	softmax (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	binary (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	competitive (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	logistic_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	rectified_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_plus_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_sign_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hard_sigmoid (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	softmax (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	binary (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	competitive (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	softmax_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

Detailed Description

This class represents a layer of perceptrons.

PerceptronLayer is a single-layer network with a hard-limit trabsfer function. This network is often trained with the perceptron learning rule.

Layers of perceptrons will be used to construct multilayer perceptrons, such as an approximation problems .

Definition at line 48 of file perceptron_layer.h.

Member Enumeration Documentation

◆ ActivationFunction

enum class ActivationFunction

strong

Enumeration of available activation functions for the perceptron neuron model.

Definition at line 56 of file perceptron_layer.h.

Constructor & Destructor Documentation

◆ PerceptronLayer() [1/2]

PerceptronLayer ( )

explicit

Default constructor. It creates a empty layer object, with no perceptrons. This constructor also initializes the rest of class members to their default values.

Definition at line 18 of file perceptron_layer.cpp.

◆ PerceptronLayer() [2/2]

PerceptronLayer	(	const Index &	new_inputs_number,
		const Index &	new_neurons_number,
		const ActivationFunction &	new_activation_function = `PerceptronLayer::ActivationFunction::HyperbolicTangent`
	)

explicit

Layer architecture constructor. It creates a layer object with given numbers of inputs and perceptrons. The parameters are initialized at random. This constructor also initializes the rest of class members to their default values.

Parameters

new_inputs_number	Number of inputs in the layer.
new_neurons_number	Number of perceptrons in the layer.

Definition at line 33 of file perceptron_layer.cpp.

◆ ~PerceptronLayer()

~PerceptronLayer ( )

virtual

Destructor. This destructor does not delete any pointer.

Definition at line 47 of file perceptron_layer.cpp.

Member Function Documentation

◆ calculate_activations()

void calculate_activations	(	const Tensor< type, 2 > &	combinations,
		Tensor< type, 2 > &	activations
	)		const

Definition at line 513 of file perceptron_layer.cpp.

◆ calculate_activations_derivatives()

void calculate_activations_derivatives	(	const Tensor< type, 2 > &	combinations,
		Tensor< type, 2 > &	activations,
		Tensor< type, 2 > &	activations_derivatives
	)		const

Definition at line 547 of file perceptron_layer.cpp.

◆ calculate_combinations()

void calculate_combinations	(	const Tensor< type, 2 > &	inputs,
		const Tensor< type, 2 > &	biases,
		const Tensor< type, 2 > &	synaptic_weights,
		Tensor< type, 2 > &	combinations
	)		const

Definition at line 489 of file perceptron_layer.cpp.

◆ calculate_error_gradient()

void calculate_error_gradient	(	const Tensor< type, 2 > &	inputs,
		LayerForwardPropagation *	forward_propagation,
		LayerBackPropagation *	back_propagation
	)		const

virtual

Reimplemented from Layer.

Definition at line 974 of file perceptron_layer.cpp.

◆ calculate_hidden_delta()

void calculate_hidden_delta	(	LayerForwardPropagation *	next_layer_forward_propagation,
		LayerBackPropagation *	next_layer_back_propagation,
		LayerBackPropagation *	layer_back_propagation
	)		const

virtual

Reimplemented from Layer.

Definition at line 655 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_lm()

void calculate_hidden_delta_lm	(	LayerForwardPropagation *	next_layer_forward_propagation,
		LayerBackPropagationLM *	next_layer_back_propagation,
		LayerBackPropagationLM *	layer_back_propagation
	)		const

virtual

Reimplemented from Layer.

Definition at line 793 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_perceptron()

void calculate_hidden_delta_perceptron	(	PerceptronLayerForwardPropagation *	next_forward_propagation,
		PerceptronLayerBackPropagation *	next_back_propagation,
		PerceptronLayerBackPropagation *	back_propagation
	)		const

Definition at line 697 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_perceptron_lm()

void calculate_hidden_delta_perceptron_lm	(	PerceptronLayerForwardPropagation *	next_forward_propagation,
		PerceptronLayerBackPropagationLM *	next_back_propagation,
		PerceptronLayerBackPropagationLM *	back_propagation
	)		const

Definition at line 835 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_probabilistic()

void calculate_hidden_delta_probabilistic	(	ProbabilisticLayerForwardPropagation *	next_forward_propagation,
		ProbabilisticLayerBackPropagation *	next_back_propagation,
		PerceptronLayerBackPropagation *	back_propagation
	)		const

Definition at line 708 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_probabilistic_lm()

void calculate_hidden_delta_probabilistic_lm	(	ProbabilisticLayerForwardPropagation *	next_forward_propagation,
		ProbabilisticLayerBackPropagationLM *	next_back_propagation,
		PerceptronLayerBackPropagationLM *	back_propagation
	)		const

Definition at line 846 of file perceptron_layer.cpp.

◆ calculate_outputs()

Tensor< type, 2 > calculate_outputs ( const Tensor< type, 2 > & inputs )

virtual

Reimplemented from Layer.

Definition at line 584 of file perceptron_layer.cpp.

◆ calculate_squared_errors_Jacobian_lm()

void calculate_squared_errors_Jacobian_lm	(	const Tensor< type, 2 > &	inputs,
		LayerForwardPropagation *	forward_propagation,
		LayerBackPropagationLM *	back_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 917 of file perceptron_layer.cpp.

◆ forward_propagate() [1/2]

void forward_propagate	(	const Tensor< type, 2 > &	inputs,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 603 of file perceptron_layer.cpp.

◆ forward_propagate() [2/2]

void forward_propagate	(	const Tensor< type, 2 > &	inputs,
		Tensor< type, 1 >	potential_parameters,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 624 of file perceptron_layer.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

virtual

Reimplemented from Layer.

Definition at line 1043 of file perceptron_layer.cpp.

◆ get_activation_function()

const PerceptronLayer::ActivationFunction & get_activation_function ( ) const

Returns the activation function of the layer. The activation function of a layer is the activation function of all perceptrons in it.

Definition at line 171 of file perceptron_layer.cpp.

◆ get_biases() [1/2]

const Tensor< type, 2 > & get_biases ( ) const

Returns the biases from all the perceptrons in the layer. The format is a vector of real values. The size of this vector is the number of neurons in the layer.

Definition at line 94 of file perceptron_layer.cpp.

◆ get_biases() [2/2]

Tensor< type, 2 > get_biases ( const Tensor< type, 1 > & parameters ) const

Definition at line 131 of file perceptron_layer.cpp.

◆ get_biases_number()

Index get_biases_number ( ) const

const Tensor< type, 2 > & get_synaptic_weights ( ) const

Returns the synaptic weights from the perceptrons. The format is a matrix of real values. The number of rows is the number of neurons in the layer. The number of columns is the number of inputs to the layer.

Definition at line 105 of file perceptron_layer.cpp.

◆ get_synaptic_weights() [2/2]

Tensor< type, 2 > get_synaptic_weights ( const Tensor< type, 1 > & parameters ) const

Definition at line 111 of file perceptron_layer.cpp.

◆ get_synaptic_weights_number()

Index get_synaptic_weights_number ( ) const

virtual

Returns the number of layer's synaptic weights.

Reimplemented from Layer.

Definition at line 76 of file perceptron_layer.cpp.

◆ insert_gradient()

void insert_gradient	(	LayerBackPropagation *	back_propagation,
		const Index &	index,
		Tensor< type, 1 > &	gradient
	)		const

virtual

Reimplemented from Layer.

Definition at line 992 of file perceptron_layer.cpp.

◆ insert_squared_errors_Jacobian_lm()

void insert_squared_errors_Jacobian_lm	(	LayerBackPropagationLM *	back_propagation,
		const Index &	index,
		Tensor< type, 2 > &	squared_errors_Jacobian
	)		const

virtual

Reimplemented from Layer.

Definition at line 958 of file perceptron_layer.cpp.

◆ set() [1/2]

void set ( )

Sets an empty layer, wihtout any perceptron. It also sets the rest of members to their default values.

Definition at line 234 of file perceptron_layer.cpp.

◆ set() [2/2]

void set	(	const Index &	new_inputs_number,
		const Index &	new_neurons_number,
		const PerceptronLayer::ActivationFunction &	new_activation_function = `PerceptronLayer::ActivationFunction::HyperbolicTangent`
	)

Sets new numbers of inputs and perceptrons in the layer. It also sets the rest of members to their default values.

Parameters

new_inputs_number	Number of inputs.
new_neurons_number	Number of perceptron neurons.

Definition at line 249 of file perceptron_layer.cpp.

◆ set_activation_function() [1/2]

void set_activation_function ( const ActivationFunction & new_activation_function )

This class sets a new activation(or transfer) function in a single layer.

Parameters

new_activation_function Activation function for the layer.

Definition at line 356 of file perceptron_layer.cpp.

◆ set_activation_function() [2/2]

void set_activation_function ( const string & new_activation_function_name )

Sets a new activation(or transfer) function in a single layer. The second argument is a string containing the name of the function("Logistic", "HyperbolicTangent", "Threshold", etc).

Parameters

new_activation_function Activation function for that layer.

Definition at line 366 of file perceptron_layer.cpp.

◆ set_biases()

void set_biases ( const Tensor< type, 2 > & new_biases )

Sets the biases of all perceptrons in the layer from a single vector.

Parameters

new_biases New set of biases in the layer.

Definition at line 318 of file perceptron_layer.cpp.

◆ set_biases_constant()

void set_biases_constant ( const type & value )

Initializes the biases of all the perceptrons in the layer of perceptrons with a given value.

Parameters

value Biases initialization value.

Definition at line 439 of file perceptron_layer.cpp.

◆ set_default()

void set_default ( )

Sets those members not related to the vector of perceptrons to their default value.

Display: True.
layer_type: Perceptron_Layer.
trainable: True.

Definition at line 271 of file perceptron_layer.cpp.

◆ set_display()

void set_display ( const bool & new_display )

Sets a new display value. If it is set to true messages from this class are to be displayed on the screen; if it is set to false messages from this class are not to be displayed on the screen.

Parameters

new_display Display value.

Definition at line 430 of file perceptron_layer.cpp.

◆ set_inputs_number()

void set_inputs_number ( const Index & new_inputs_number )

virtual

Sets a new number of inputs in the layer. The new synaptic weights are initialized at random.

Parameters

new_inputs_number Number of layer inputs.

Reimplemented from Layer.

Definition at line 291 of file perceptron_layer.cpp.

◆ set_name()

void set_name ( const string & new_layer_name )

Definition at line 281 of file perceptron_layer.cpp.

◆ set_neurons_number()

void set_neurons_number ( const Index & new_neurons_number )

virtual

Sets a new number perceptrons in the layer. All the parameters are also initialized at random.

Parameters

new_neurons_number New number of neurons in the layer.

Reimplemented from Layer.

Definition at line 305 of file perceptron_layer.cpp.

◆ set_parameters()

void set_parameters	(	const Tensor< type, 1 > &	new_parameters,
		const Index &	index = `0`
	)

virtual

Sets the parameters of this layer.

Reimplemented from Layer.

Definition at line 338 of file perceptron_layer.cpp.

◆ set_parameters_constant()

void set_parameters_constant ( const type & value )

virtual

Initializes all the biases and synaptic weights in the neural newtork with a given value.

Parameters

value Parameters initialization value.

Reimplemented from Layer.

Definition at line 457 of file perceptron_layer.cpp.

◆ set_parameters_random()

void set_parameters_random ( )

virtual

Initializes all the biases and synaptic weights in the neural newtork at random with values comprised between -1 and +1.

Reimplemented from Layer.

Definition at line 468 of file perceptron_layer.cpp.

◆ set_synaptic_weights()

void set_synaptic_weights ( const Tensor< type, 2 > & new_synaptic_weights )

Sets the synaptic weights of this perceptron layer from a single matrix. The format is a matrix of real numbers. The number of rows is the number of neurons in the corresponding layer. The number of columns is the number of inputs to the corresponding layer.

Parameters

new_synaptic_weights New set of synaptic weights in that layer.

Definition at line 330 of file perceptron_layer.cpp.

◆ set_synaptic_weights_constant()

void set_synaptic_weights_constant ( const type & value )

Initializes the synaptic weights of all the perceptrons in the layer of perceptrons with a given value.

Parameters

value Synaptic weights initialization value.

Definition at line 448 of file perceptron_layer.cpp.

◆ write_activation_function()

string write_activation_function ( ) const

Returns a string with the name of the layer activation function. This can be: Logistic, HyperbolicTangent, Threshold, SymmetricThreshold, Linear, RectifiedLinear, ScaledExponentialLinear.

Definition at line 180 of file perceptron_layer.cpp.

◆ write_activation_function_expression()

string write_activation_function_expression ( ) const

Definition at line 1224 of file perceptron_layer.cpp.

◆ write_activations_c()

string write_activations_c ( ) const

Todo:

Definition at line 1291 of file perceptron_layer.cpp.

◆ write_activations_python()

string write_activations_python ( ) const

Todo:

Definition at line 1382 of file perceptron_layer.cpp.

◆ write_combinations_c()

string write_combinations_c ( ) const

Definition at line 1266 of file perceptron_layer.cpp.

◆ write_combinations_python()

string write_combinations_python ( ) const

Definition at line 1355 of file perceptron_layer.cpp.

◆ write_expression()

string write_expression	(	const Tensor< string, 1 > &	inputs_names,
		const Tensor< string, 1 > &	outputs_names
	)		const

virtual

Returns a string with the expression of the inputs-outputs relationship of the layer.

Parameters

inputs_names	vector of strings with the name of the layer inputs.
outputs_names	vector of strings with the name of the layer outputs.

Reimplemented from Layer.

Definition at line 1016 of file perceptron_layer.cpp.

◆ write_expression_c()

string write_expression_c ( ) const

virtual

Reimplemented from Layer.

Definition at line 1447 of file perceptron_layer.cpp.

◆ write_expression_python()

string write_expression_python ( ) const

virtual

Reimplemented from Layer.

Definition at line 1463 of file perceptron_layer.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

virtual

Reimplemented from Layer.

Definition at line 1154 of file perceptron_layer.cpp.

Member Data Documentation

◆ activation_function

ActivationFunction activation_function

protected

Activation function variable.

Definition at line 247 of file perceptron_layer.h.

◆ biases

Tensor<type, 2> biases

protected

Bias is a neuron parameter that is summed with the neuron's weighted inputs and passed through the neuron's transfer function to generate the neuron's output.

Definition at line 239 of file perceptron_layer.h.

◆ display

bool display = true

protected

Display messages to screen.

Definition at line 251 of file perceptron_layer.h.

◆ synaptic_weights

Tensor<type, 2> synaptic_weights

protected

This matrix containing conection strengths from a layer's inputs to its neurons.

Definition at line 243 of file perceptron_layer.h.

The documentation for this class was generated from the following files:

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

◆ ActivationFunction

Constructor & Destructor Documentation

◆ PerceptronLayer() [1/2]

◆ PerceptronLayer() [2/2]

◆ ~PerceptronLayer()

Member Function Documentation

◆ calculate_activations()

◆ calculate_activations_derivatives()

◆ calculate_combinations()

◆ calculate_error_gradient()

◆ calculate_hidden_delta()

◆ calculate_hidden_delta_lm()

◆ calculate_hidden_delta_perceptron()

◆ calculate_hidden_delta_perceptron_lm()

◆ calculate_hidden_delta_probabilistic()

◆ calculate_hidden_delta_probabilistic_lm()

◆ calculate_outputs()

◆ calculate_squared_errors_Jacobian_lm()

◆ forward_propagate() [1/2]

◆ forward_propagate() [2/2]

◆ from_XML()

◆ get_activation_function()

◆ get_biases() [1/2]

◆ get_biases() [2/2]

◆ get_biases_number()

◆ get_display()

◆ get_inputs_number()

◆ get_neurons_number()

◆ get_parameters()

◆ get_parameters_number()

◆ get_synaptic_weights() [1/2]

◆ get_synaptic_weights() [2/2]

◆ get_synaptic_weights_number()

◆ insert_gradient()

◆ insert_squared_errors_Jacobian_lm()

◆ set() [1/2]

◆ set() [2/2]

◆ set_activation_function() [1/2]

◆ set_activation_function() [2/2]

◆ set_biases()

◆ set_biases_constant()

◆ set_default()

◆ set_display()

◆ set_inputs_number()

◆ set_name()

◆ set_neurons_number()

◆ set_parameters()

◆ set_parameters_constant()

◆ set_parameters_random()

◆ set_synaptic_weights()

◆ set_synaptic_weights_constant()

◆ write_activation_function()

◆ write_activation_function_expression()

◆ write_activations_c()

◆ write_activations_python()

◆ write_combinations_c()

◆ write_combinations_python()

◆ write_expression()

◆ write_expression_c()

◆ write_expression_python()

◆ write_XML()

Member Data Documentation

◆ activation_function

◆ biases

◆ display

◆ synaptic_weights